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Learning an opponent's preferences in bilateral multi-issue automated negotiations can lead to more favorable outcomes. However, existing opponent models can fail in negotiation contexts when their assumptions about opponent behaviors differ from actual behavior patterns. Although integrating broader behavioral assumptions into these models could be beneficial, it poses a challenge because the models are designed with specific assumptions. Therefore, this study proposes an adaptable opponent model that integrates a general behavioral assumption. Specifically, the proposed model uses Bayesian learning (BL), which can apply various behavioral assumptions by considering the opponent's entire bidding sequence. However, this BL model is computationally infeasible for multi-issue negotiations. Hence, current BL models often impose constraints on their hypothesis space, but these constraints about the utility function's shape significantly sacrifice accuracy. This study presents a novel scalable BL model that relaxes these constraints to improve accuracy while maintaining linear time complexity by separately learning each parameter of a utility function. Furthermore, we introduce a general assumption that the opponent's bidding strategy follows a concession-based pattern to enhance adaptability to various negotiation contexts. We explore three likelihood function options to implement this assumption effectively. By incorporating these options into the proposed scalable model, we develop three scalable concession-driven opponent models using Bayesian learning (COMB). Experiments across 45 negotiation domains using 15 basic agents and 15 finalists from the automated negotiating agents competition demonstrate the proposed scalable model's higher accuracy than existing scalable models. COMB models show higher adaptability to various negotiation contexts than state-of-the-art models. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as a decentralized computing paradigm that extends cloud services to the network edge, enabling faster data processing and real-time applications. The increasing popularity of fog computing has led to the emergence of a potential market involving users and providers of fog resources. However, both parties are driven by self-interest and seek to maximize their utility, giving rise to multiple conflicts extending beyond mere price considerations. Negotiations can play a crucial role in resolving conflicts and establishing mutually beneficial service level agreements. Moreover, in the heterogeneous fog environment, quality of service attributes, such as throughput, delay, trust, power dissipation, etc., vary significantly among different user-fog associations. These attributes, although non-negotiable, hold great importance for entities and directly influence partner selection. Entities may exhibit a preference for one another based on these non-negotiable attributes. To the best of our knowledge, no existing literature specifically addresses the issue of associating with a preferred trading partner at a negotiated value for multiple issues in the fog environment. This research aims to address this gap and provide insights into this unexplored area. This work presents a novel Preference-based Muti-Issue Negotiation Algorithm, PMINA, for many to many, bilateral and concurrent negotiations in the fog environment. The results confirm the significance of PMINA, demonstrating a substantial enhancement in user and fog utilities. [ABSTRACT FROM AUTHOR]

Automated negotiation is an efficient approach for interaction in multi-agent systems in which agents exchange offers and counteroffers to conclude an agreement. This paper addresses the problem of offer formulation during the interaction between buyer and seller software agents for the purpose of reaching an agreement over quantitative and qualitative issues at once. In order to improve the outcome of the negotiation process, a hybrid negotiation method is presented and verified. Offer formulation is based on fuzzy similarity and preference-based methods. The preference-based mechanism is used for quantitative issues, while the fuzzy similarity technique is used for qualitative issues. The preference-based mechanism takes into account the preferences of the opponent when generating offers; the agent makes greater concessions on the issues which the opponent prefers more. The fuzzy-similarity method formulates an offer that considers offering a deal that is more similar to the one received by the opponent during the last round of negotiation. The experiments consists of two parts. The first part compares the hybrid strategy with the basic one. The findings reveal that the hybrid strategy is better in all performance measures, namely, utility rate, agreement rate, and Nash product rate. The second part of the experimental work compares four mechanisms of offer generating mechanisms: basic, preference-based, fuzzy similarity, and hybrid. The results show that the hybrid negotiation strategy performs equal or better that other negotiation strategies. More details can be found in the paper. [ABSTRACT FROM AUTHOR]

The work presented in this paper describes a multi-issue negotiation protocol between active consumers and a management platform in order to establish load coordination in a highly congested network. The multi-issue negotiation protocol considers simultaneously the consumption interval, the price, and the size of the energy packages, which is the main contribution of this work. Regarding the implementation methodology, the proposed algorithms have been implemented using MATLAB classes that allow emulating the behaviour of a multi-agent system in which each vehicle is an agent that interacts with the platform, which is another agent. In the present work, and without loss of generality, the algorithm is applied to coordinate the charging of electric vehicles (EVs) in a distribution network in which building loads represent critical loads. The algorithm is tested in a realistic environment, and its stability and performance are evaluated. Furthermore, the description of the algorithm is provided in a generic form, and it could be applied to any other scenario. [ABSTRACT FROM AUTHOR]

Researchers have shown that structuring issues and organizing an agenda before a negotiation lead to improved negotiation performance. By using issue analysis, negotiators become aware of their own and their opponents' preferences on negotiation issues and are able to use this knowledge to optimize their degree of success. Following research on asymmetrical preferences in negotiations, we introduce a new approach for issue analysis that considers the identification of one‐sided preferences, specifically a 0‐preference for issues from one party. We conducted an experimental study to test if this type of preference for an issue (chance issue) yields strategic potential for a negotiator. We also examined whether the identification of these chance issues could be particularly relevant for a low‐power party in negotiations with a power imbalance, to overcome the lower scope of action due to the weaker negotiating position. The results indicate initial verification that no preference at all for one issue could lead to higher individual performance and noneconomic outcomes. Joint performance was positively affected by 0‐preference, even in unbalanced power situations. [ABSTRACT FROM AUTHOR]

This article presents a scalable mechanism for peer-to-peer (P2P) energy trading among prosumers in a smart grid. In the proposed mechanism, prosumers engage in a non-mediated negotiation with their peers to reach an agreement on the price and quantity of energy to be exchanged. Instead of concurrent bilateral negotiation between all peers with high overheads, an iterative peer matching process is employed to match peers for bilateral negotiation. The proposed negotiation algorithm enables prosumers to come to an agreement, given that they have no prior knowledge about the preference structure of their trading partners. A greediness factor is introduced to model the selfish behavior of prosumers in the negotiation process and to investigate its impact on the negotiation outcome. In order to recover the costs related to power losses, a transaction fee is applied to each transaction that enables the grid operator to recover incurred losses due to P2P trades. The case studies demonstrate that the proposed mechanism discourages greedy behavior of prosumers in the negotiation process as it does not increase their economic surplus. Also, it has an appropriate performance from the computation overheads and scalability perspectives. [ABSTRACT FROM AUTHOR]

More extensive enterprise cooperation is an effective means to increase the competitiveness of shipbuilding companies in the current distributed manufacturing environment. Most cross-enterprise collaborative processes for shipbuilding projects have been widely concerned. However, the symmetry and cooperativity among the order decision-making process is rarely involved. A key issue for decision makers is to balance the interests of each symmetric participant and realize the consistent decision-making for the order. Existing order decision-making methods in the shipbuilding industry are low efficiency. The aim of this paper is to provide an assistant decision-making method to support effective order decision-making and multi-party cooperation for the multi-wining negotiation objectives. To solve this problem, a collaborative order decision-making framework based on decision support system (DSS) and multi-agent system (MAS) theory is presented, simulating the collaborative order decision-making process, and bridging the order decision-making with production scheduling. Then, a multi-stage negotiation method is provided to solve the distributed and symmetric order decision-making problem, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system will be reported as a result.

Negotiation is both an important topic in multi-agent systems research and an important aspect of daily life. Many real-world negotiations are complex and involve multiple interdependent issues, therefore, there has been increasing interest in such negotiations. Existing nonlinear automated negotiation protocols have difficulty in finding solutions when the number of issues and agents is large. In automated negotiations covering multiple independent issues, it is useful to separate out the issues and reach separate agreements on each in turn. In this paper, we propose an effective approach to automated negotiations based on recursive partitioning using an issue dendrogram. A mediator first finds partial agreements in each sub-space based on bids from the agents, then combines them to produce the final agreement. When it cannot find a solution, our proposed method recursively decomposes the negotiation sub-problems using an issue dendrogram. In addition, it can improve the quality of agreements by considering previously-found partial consensuses. We also demonstrate experimentally that our protocol generates higher-optimality outcomes with greater scalability than previous methods. [ABSTRACT FROM AUTHOR]

Throughout the study of bilateral negotiation, researchers have strived to reach an agreement in a simplified scenario: with definite utility functions (linear or polynomial) or over independent issues, which in realistic is not that simple. To remove this limitation, in this paper, we illustrate a complex nonlinear utility space: bilateral negotiations over many issues with inter- dependent valuations. An iterative and fair protocol is proposed to explore it, seeking for a 'win-win' and 'envy-free' contract to both agents. It works by introducing a utility secret transportation mechanism, combined with the heuristic algorithm NSGA-II, to find the Pareto efficient outcomes without the exposure of negotiators' utility preferences. Experimental results analyze the benefit of employing Nash Product in the proposed protocol, and demonstrate its strengths in optimality rate and negotiating efficiency. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to propose a warranty-based bilateral automated multi-issue negotiation approach. Design/methodology/approach – A methodology for bilateral automated negotiation process is developed considering the targets such as warranty attractiveness, warranty cost, mean time between failures, spare parts cost to the end user over the useful life of the life. The negotiation methodology is explained using different cases of negotiation. The optimization for each negotiation step is carried out using genetic algorithm with elitism strategy. Findings – The result after optimization indicates that the desired target values are achieved and manufacturer obtained desired profit margin. Practical implications – Application of automated negotiation model is illustrated using a real life case of an automobile engine manufacturer. The proposed approach helps the manufacturer of any product to develop a methodology for carrying out the negotiation process. The approach also results into taking warranty-related decisions at the design stage. Originality/value – This paper contributes in proposing a generalized methodology for warranty-based negotiation in which the negotiation is carried out between the manufacturer and the customer. [ABSTRACT FROM AUTHOR]

Agent negotiation is a form of decision making where two or more agents jointly search for a mutually agreed solution to a certain problem. In multi-issue negotiation, with information available about the agents’ preferences, a negotiation may result in a mutually beneficial agreement. In a competitive negotiation environment, however, self-interested agents may not be willing to reveal their preferences, and this can increase the difficulty of negotiating a mutually beneficial agreement. In order to solve this problem, this paper proposes a Bayesian-based approach which can help an agent to predict its opponent’s preference in bilateral multi-issue negotiation. The proposed approach employs Bayesian theory to analyse the opponent’s historical offers and to approximately predict the opponent’s preference over negotiation issues. A counter-offer proposition algorithm is also integrated into the prediction approach to help agents to propose mutually beneficial offers based on the prediction results. Experimental results indicate good performance of the proposed approach in terms of utility gain and negotiation efficiency. [ABSTRACT FROM AUTHOR]

Since participants in a Cloud may be independent bodies, some mechanisms are necessary for resolving the different preferences to establish a service-level agreement (SLA) for Cloud service reservations. Whereas there are some mechanisms for supporting SLA negotiation, there is little or no negotiation support involving price, time slot, and QoS issues concurrently for a Cloud service reservation. For the concurrent price, timeslot, and QoS negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price, timeslot, and QoS proposals is necessary. The contribution of this work is designing a multi-issue negotiation mechanism to facilitate 1) concurrent price, time slot, and QoS negotiations including the design of QoS utility functions and 2) adaptive and similarity-based trade-off proposals for price, time slots, and level of QoS issues. The tradeoff algorithm referred to as 'adaptive burst mode' is especially designed to increase negotiation speed, total utility, and to reduce computational load for evaluating proposals by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using an agent-based testbed suggest that using the negotiation mechanism, (i) a consumer and a provider agent have a mutually satisfying agreement on price, time slot, and QoS issues in terms of the aggregated utility and (ii) the fastest negotiation speed with (iii) comparatively lower number of evaluated proposals in a negotiation. [ABSTRACT FROM AUTHOR]

Most real-world negotiation involves multiple interdependent issues, which makes an agent's utility functions nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; they cannot find a high-quality solution when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the utility space into several largely independent subspaces. In this paper, we propose a method for decomposing a utility space based on every agent's utility space. In addition, the mediator finds the contracts in each group based on the votes from all agents, and combines the contract in each issue-group. This method allows good outcomes with greater scalability than the method without issue-grouping. We demonstrate that our protocol, based on issue-groups, has a higher optimality rate than previous efforts, and discuss the impact on the optimality of the negotiation outcomes. [ABSTRACT FROM AUTHOR]

Abstract: In complex product design environments, new product development (NPD) becomes all about trade-offs and therefore trade-off management gets an important responsibility for designers. Correspondingly, in these environments, customer co-creation in NPD requires multi-issue negotiation with issue trade-offs. However, there is no study researching customer co-creation in NPD through multi-issue negotiation with issue trade-offs according to our best knowledge. This study presents a framework for customer co-creation in NPD through a multi-issue negotiation mechanism, namely; Modified Even-Swaps, to fill this gap. This mechanism uses a modified version of Even-Swaps method, that finds differences among alternatives and utilizes the differences for assessing the value of multi-issue offers, and that includes a fuzzy-inference-system for bargaining on several issues simultaneously. In the proposed framework, customers are represented by “lead-user”. Thus, negotiation is performed between lead-user and designer. In order to show how the framework works, an illustrative example is also presented. [Copyright &y& Elsevier]

This paper simulates the multi-issue negotiation with fuzzy qualitative simulation method (FQSIM) and constructs the model of multi-issue negotiation based on FQSIM. The fuzzy qualitative simulation process of the model is validated by an example, and we could find that the process was according with the logic of real negotiation and the intuitionistic conjecture of people through the validation result, which could reflect the change direction clearly. The model has good adaptability to real negotiation and had settled the problems that were engendered in traditional quantitive simulation by uncertain factor. The model could reduce the complexity of multi-issue negotiation, and enhance the credibility and usefulness of simulation model. [ABSTRACT FROM AUTHOR]

The article discusses the study on agent multi-issue negotiation through nonlinear utility functions. It mentions the implementation of proposed utility function which is a concave function in microeconomics. It tackles the proposed greedy algorithm and the negotiation model with two phases in a Pareto optimal result.

When making reservations for Cloud services, consumers and providers need to establish service-level agreements through negotiation. Whereas it is essential for both a consumer and a provider to reach an agreement on the price of a service and when to use the service, to date, there is little or no negotiation support for both price and time-slot negotiations (PTNs) for Cloud service reservations. This paper presents a multi-issue negotiation mechanism to facilitate the following: 1) PTNs between Cloud agents and 2) tradeoff between price and time-slot utilities. Unlike many existing negotiation mechanisms in which a negotiation agent can only make one proposal at a time, agents in this work are designed to concurrently make multiple proposals in a negotiation round that generate the same aggregated utility, differing only in terms of individual price and time-slot utilities. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Using the testbed, experiments were carried out to compare this work with related approaches. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches. A case study was carried out to demonstrate the application of the PTN mechanism for pricing Cloud resources. [ABSTRACT FROM AUTHOR]

Negotiation with multiple interdependent issues is an important problem since much of real-world negotiation falls into this category. This paper examines the problem that, in such domains, agent utility functions are nonlinear, and thereby can create nonconvex Pareto frontiers. This in turn implies that the Nash Bargaining Solution, which has been viewed as the gold standard for identifying a unique optimal negotiation outcome, does not serve that role in nonlinear domains. In nonlinear domains, unlike linear ones, there can be multiple Nash Bargaining Solutions, and all can be sub-optimal with respect to social welfare and fairness. In this paper, we propose a novel negotiation protocol called SFMP (the Secure and Fair Mediator Protocol) that addresses this challenge, enabling secure multilateral negotiations with fair and pareto-optimal outcomes in nonlinear domains. The protocol works by (1) using nonlinear optimization, combined with a Multi-Party protocol, to find the Pareto front without revealing agent's private utility information, and (2) selecting the agreement from the Pareto set that maximizes a fair division criterion we call approximated fairness. We demonstrate that SFMP is able to find agreements that maximize fairness and social welfare in nonlinear domains, and out-performs (in terms of outcomes and scalability) previously developed nonlinear negotiation protocols. [ABSTRACT FROM AUTHOR]

The 'negotiation dance', as Raiffa calls the dynamic pattern of the bidding, has an important influence on the outcome of the negotiation. The current practice of evaluating a negotiation strategy is to focus on fairness and quality aspects of the agreement. In this article we present the framework DANS (Dynamics Analysis of Negotiation Strategies) for the analysis of the dynamic patterns of the bidding as a means to evaluate the strengths and weaknesses of negotiation strategies for bidding. The method provides the tools to perform a detailed and quantified analysis of a negotiation between two agents in terms of dynamic properties of the negotiation trace. The classification of negotiation steps in the dance plays a central role in the analysis. The method can be applied to tournaments, but can also be used to analyze single 1-on-1 negotiation sessions. The sessions can be played by humans or by software agents. Using DANS we show that some strategies are sensitive to the bidding behaviour of the opponent, and some depend on a correct model of the opponent. DANS helped us in discovering that domain characteristics are important for the analysis of strategies. Some strategies rely heavily on some domain assumptions. Furthermore, the results illustrate that having domain knowledge is not always enough to avoid making unintentional steps. The method is demonstrated in the analysis of three strategies from the literature ABMP, Trade-Off and Bayesian Agent. [ABSTRACT FROM AUTHOR]

In multi-issue negotiations, issues may be negotiated independently or not. In the latter case, the utility associated with one issue depends on the value of another. Searching for good bids in a utility space based on multiple, dependent issues is in general intractable. Furthermore, existing negotiation strategies have proven to be efficient for negotiation in domains with independent issues cannot be used in case of dependencies between issues. To deal with this increased complexity, one can introduce a mediator in the negotiation setting, increase the power of computers exponentially, or approximate the utility space. Given the number of high quality and tractable algorithms that exist for independent issue sets, in this paper an approach that approximates the complex utility space by eliminating the dependencies is proposed. The approximated spaces can be used by existing negotiation algorithms that proved their worth in domains without issues dependencies. The approach exploits knowledge of experienced negotiators that determines for a domain and a set of negotiating parties the expected outcome range. The more specialised the knowledge of the negotiator, the more narrow the expected outcome range, and the more precise our approximation. Using the approximated space instead of the original without any further safeguards would be risky; what seems a good deal in the approximated space might be a bad deal in the original space. To mitigate this risk we introduce a safety procedures that interfere both in the approximation phase and in the bidding phase. The first safety procedure tunes the parameters of the approximation procedure to control the outcome deviation. We show how these parameters can be used to balance computational cost and accuracy of negotiation outcome. Based on experimental results specific values for the parameters are determined that, in general, provide a good balance between computational costs and accuracy. The second safeguard consists of a bid search wrapper algorithm that ensures that during the bidding no mistakes are made that are related to the use of the approximated space. Our approach is based on the assumption that the typical structure of general negotiation spaces is "near linear", making it worth to do the approximation and checking its accuracy. [ABSTRACT FROM AUTHOR]

Multi-issue negotiation protocols represent a promising field since most negotiation problems in the real world involve multiple issues. Our work focuses on negotiation with interdependent issues in which agent utility functions are nonlinear. Existing works have not yet focused on the private information of agents. In addition, they were not scalable in the sense that they have shown a high failure rate for making agreements among five or more agents. In this paper, we focus on a novel multi-round representative based protocol that utilizes the amount of revealed agents' private information. Experimental results demonstrate that our mechanism reduces the failure rate in making agreements and is scalable for the number of agents compared with existing approaches. [ABSTRACT FROM AUTHOR]

This paper presents a method for acquiring a concession strategy of an agent in multi-issue negotiation. This method learns how to make a concession to an opponent for realizing win-win negotiation. To learn the concession strategy, we adopt reinforcement learning. First, an agent receives a proposal from an opponent. The agent recognizes a negotiation state using the difference between their proposals and the difference between their concessions. According to the state, the agent makes a proposal by reinforcement learning. A reward of the learning is a profit of an agreement and a punishment of negotiation breakdown. The experimental results showed that the agents could acquire the negotiation strategy that avoids negotiation breakdown and increases profits of an agreement. As a result, agents can acquire the action policy that strikes a balance between cooperation and competition. [ABSTRACT FROM AUTHOR]

This paper reports on experiments in (human) multi-issue negotiation and their analysis, and presents a generic software environment supporting such an analysis. First, the paper presents a System for Analysis of Multi-Issue Negotiation (SAMIN). SAMIN was designed to analyse multi-issue negotiation processes between human negotiators, between human and software agents, and between software agents. The agents conduct one-to-one negotiations, in which the values across multiple issues are negotiated on simultaneously. To analyse such negotiation processes, the user can enter any formal property deemed useful into the system and use the system to automatically check this property in given negotiation traces. The paper presents the results of applying SAMIN in the analysis of empirical traces obtained from an experiment in multi-issue negotiation about second hand cars. In the experiment the efforts of 74 humans negotiating against each other have been analysed using SAMIN. [ABSTRACT FROM AUTHOR]

Multi-Issue Negotiation protocols have been studied very widely and represent a promising field since most of negotiation problems in the real-world are complex ones including multiple issues. In particular, in reality issues are constrained each other. This makes agents' utilities nonlinear. There have been a lot of work on multi-issue negotiations. However, there have been very few work that focus on nonlinear utility spaces. In this paper, we assume agents have nonlinear utility spaces. For the linear utility domain, agents can aggregate the utilities of the issue-values by simple linear summation. In the real world, such aggregations are unrealistic. For example, we cannot just add up the value of car's tires and the value of car's engine when engineers design a car. In this paper, we propose an auction-based multiple-issue negotiation protocol among nonlinear utility agents. Our negotiation protocol employs several techniques, i.e., adjusting sampling, auction-based maximization of social welfare. Our experimental results show that our method can outperform the existing simple methods in particular in the huge utility space that can be often found in the real-world. Further, theoretically, our negotiation protocol can guarantee the completeness if some conditions are satisfied. [ABSTRACT FROM AUTHOR]